





import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:Union[str, Any]      ,				snake_case__	:int=1024      ,				snake_case__	:List[str]=1024      ,				snake_case__	:int=False      ,				**snake_case__	:Tuple  )   ->    Tuple:
  _lowercase						    =			AutoTokenizer.from_pretrained(snake_case__  )
  _lowercase						    =			SeqaSeqDataset(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				type_path='train'      ,				**snake_case__  )
  _lowercase						    =			tok.pad_token_id

  def get_lens(snake_case__	:Optional[Any]  ):
    _lowercase						    =			tqdm(
        DataLoader(snake_case__      ,				batch_size=512      ,				num_workers=8      ,				shuffle=snake_case__      ,				collate_fn=ds.collate_fn  )      ,				desc=str(ds.len_file  )      ,				)
    _lowercase						    =			[]
    for batch in dl:
      _lowercase						    =			batch['input_ids'].ne(snake_case__  ).sum(1  ).tolist()
      _lowercase						    =			batch['labels'].ne(snake_case__  ).sum(1  ).tolist()
      if consider_target:
        for src, tgt in zip(snake_case__      ,				snake_case__  ):
          max_lens.append(max(snake_case__      ,				snake_case__  )  )
      else:
        max_lens.extend(snake_case__  )
    return max_lens

  _lowercase						    =			get_lens(snake_case__  )
  _lowercase						    =			SeqaSeqDataset(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				type_path='val'      ,				**snake_case__  )
  _lowercase						    =			get_lens(snake_case__  )
  pickle_save(snake_case__      ,				train_ds.len_file  )
  pickle_save(snake_case__      ,				val_ds.len_file  )


if __name__ == "__main__":
      fire.Fire(save_len_file)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






